Geo-Specific Visualization of Retail and Online Sales

library(ggmap)
Loading required package: ggplot2
Google Maps API Terms of Service: http://developers.google.com/maps/terms.
Please cite ggmap if you use it: see citation('ggmap') for details.
library(ggplot2)
library(zipcode)
data_dc <- read.delim(file='//10.8.8.51/lv0/Move to Box/Mithun/projects/9.BR_US_DS_Project/1.data/BR_disc_sens_geo_data.txt',
                      sep = '|',header = F, col.names = c('customer_key',
'percent_disc_last_12_mth',
'br_net_sales_amt_12_mth',
'flag_employee',
'addr_zip_code',
'resp_disc_percent',
'browse_hit_ind_tot',
'purchased',
'br_online_sales12_mth',
'br_offline_sales_12mth'))
data_dc$br_retail_percent <-  data_dc$br_offline_sales_12mth / data_dc$br_net_sales_amt_12_mth
data_dc$br_online_percent <-  data_dc$br_online_sales12_mth / data_dc$br_net_sales_amt_12_mth
data_dc$br_gross_sales_amt <-  data_dc$br_net_sales_amt_12_mth / (1 - data_dc$percent_disc_last_12_mth)

Plot Data as per PIN code

library(dplyr)

Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>

The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:

    filter, lag

The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:

    intersect, setdiff, setequal, union
zip_wise_data <- data_dc[,c("br_offline_sales_12mth","br_online_sales12_mth","br_net_sales_amt_12_mth","br_gross_sales_amt","addr_zip_code")] %>% group_by(addr_zip_code) %>% summarise(br_online_sales12_mth = sum(br_online_sales12_mth,na.rm=T), br_offline_sales_12mth = sum(br_offline_sales_12mth,na.rm=T), br_net_sales_amt_12_mth = sum(br_net_sales_amt_12_mth,na.rm=T), br_gross_sales_amt = sum(br_gross_sales_amt,na.rm=T))
zip_wise_data <- zip_wise_data[zip_wise_data$addr_zip_code!='',]
library(zipcode)
data("zipcode")
data_dc2<- inner_join(data_dc,zipcode,by=c("addr_zip_code"="zip"))
joining factor and character vector, coercing into character vector
library("ggplot2")
zip_wise_data <- left_join(zipcode,zip_wise_data,by=c("zip"="addr_zip_code"))
joining factor and character vector, coercing into character vector

Discount Rate by ZipCode

zip_wise_data$disc_percent <- (1 - zip_wise_data$br_net_sales_amt_12_mth/zip_wise_data$br_gross_sales_amt )
zip_wise_data$disc_percent[is.na(zip_wise_data$disc_percent)] <- 0
zip_wise_data$disc_percent[zip_wise_data$disc_percent > 1] <- 1
zip_wise_data$disc_percent[zip_wise_data$disc_percent < 0] <- 0
zip_wise_data$Online_percent<-zip_wise_data$br_online_sales12_mth/zip_wise_data$br_net_sales_amt_12_mth
zip_wise_data$Online_percent[is.na(zip_wise_data$Online_percent)] <- 0
zip_wise_data$Online_percent[zip_wise_data$Online_percent > 1] <- 1
zip_wise_data$Online_percent[zip_wise_data$Online_percent < 0] <- 0
zip_wise_data$binx <- cut(zip_wise_data$disc_percent,breaks = c(-Inf,0,0.20,0.35,0.40,Inf),include.lowest = T,
                          labels = c("No_Info","a_Low","b_Medium","c_High","d_VeryHigh"))
zip_wise_data <-zip_wise_data[which(zip_wise_data$latitude < 50 & zip_wise_data$latitude > 25 &
                                    zip_wise_data$longitude > -125 &  zip_wise_data$longitude < -66),]
ggplot(data=zip_wise_data) + geom_point(aes(x=longitude, y=latitude, colour=binx)) +ggtitle(" Discount Rate  ZIPCODE wise") +theme(legend.position ="bottom")

ggplot(zip_wise_data, aes(x = longitude, y = latitude,  fill= disc_percent )) + geom_polygon(colour = "white", size = 0.1) + theme(legend.position ="bottom")

ggplot(zip_wise_data, aes(x = longitude, y = latitude,  fill= Online_percent )) + geom_polygon(colour = "white", size = 0.1) + theme(legend.position ="bottom")

Discount Bin 10 is highest Discount

require(ggplot2)
require(maptools)
mapcounties <- map_data("county")
mapstates <- map_data("state")

counties <- map('county', fill=TRUE, col="transparent", plot=FALSE)
IDs <- sapply(strsplit(counties$names, ":"), function(x) x[1])
counties_sp <- map2SpatialPolygons(counties, IDs=IDs,
                                   proj4string=CRS("+proj=longlat +datum=WGS84"))

pointsSP <- SpatialPoints(as.data.frame(zip_wise_data[,c('longitude','latitude')]),
proj4string=CRS("+proj=longlat +datum=WGS84"))
indices <- over(pointsSP, counties_sp)

countyNames <- sapply(counties_sp@polygons, function(x) x@ID)

zip_wise_data$county<-countyNames[indices]
# Generate Comma Separated County/Subregion list for all Counties of US
#
mapcounties$county <- with(mapcounties , paste(region, subregion, sep = ","))

# Aggregate our data at County Level (it was earlier aggreated at zipcode level)
#
zip_wise_data.sum<- zip_wise_data %>% group_by(county) %>%  summarize(br_online_sales12_mth = sum(br_online_sales12_mth,na.rm=T), br_offline_sales_12mth = sum(br_offline_sales_12mth,na.rm=T), br_net_sales_amt_12_mth = sum(br_net_sales_amt_12_mth,na.rm=T), br_gross_sales_amt = sum(br_gross_sales_amt,na.rm=T),disc_percent=median(disc_percent,na.rm=T),Online_percent=median(Online_percent))


# Remove any duplicate records
#
zip_wise_data.un<-zip_wise_data.sum[!duplicated(zip_wise_data[c("county")]),]

zip_wise_data.final=merge(zip_wise_data.sum,zip_wise_data.un,by.x='county',by.y = 'county')

zip_wise_data.final$disc_perc_bin <- as.factor(ntile(zip_wise_data.final$disc_percent.x,5))
# Merge our data with all US counties data
#
zip_wise_data.final$Online_percent_bin <- as.factor(ntile(zip_wise_data.final$Online_percent.x,5))
mergedata <- merge(mapcounties, zip_wise_data.final, by.x = "county", by.y = "county")

High Discount Rate County Wise

High Online Sale Percent County Wise

Statewide map

zip_wise_data_state <- inner_join(st_code_file,zip_wise_data_state,by=c("state_code"="state"))
joining factor and character vector, coercing into character vector

Discount and % of Online Sales Visualization by State

% Online Sales by State

Discount Percent taken accross United States

Online Sale Percent over overall sales accross United States

---
title: ' Geo Location Wise Retail and Online Sale Visualization'
output:
  html_notebook: default
  html_document: default
  pdf_document: default
---

Geo-Specific Visualization of Retail and Online Sales

```{r}
library(ggmap)
library(ggplot2)
library(zipcode)

data_dc <- read.delim(file='//10.8.8.51/lv0/Move to Box/Mithun/projects/9.BR_US_DS_Project/1.data/BR_disc_sens_geo_data.txt',
                      sep = '|',header = F, col.names = c('customer_key',
'percent_disc_last_12_mth',
'br_net_sales_amt_12_mth',
'flag_employee',
'addr_zip_code',
'resp_disc_percent',
'browse_hit_ind_tot',
'purchased',
'br_online_sales12_mth',
'br_offline_sales_12mth'))

data_dc$br_retail_percent <-  data_dc$br_offline_sales_12mth / data_dc$br_net_sales_amt_12_mth

data_dc$br_online_percent <-  data_dc$br_online_sales12_mth / data_dc$br_net_sales_amt_12_mth

data_dc$br_gross_sales_amt <-  data_dc$br_net_sales_amt_12_mth / (1 - data_dc$percent_disc_last_12_mth)

```
Plot Data as per PIN code
```{r}
library(dplyr)
zip_wise_data <- data_dc[,c("br_offline_sales_12mth","br_online_sales12_mth","br_net_sales_amt_12_mth","br_gross_sales_amt","addr_zip_code")] %>% group_by(addr_zip_code) %>% summarise(br_online_sales12_mth = sum(br_online_sales12_mth,na.rm=T), br_offline_sales_12mth = sum(br_offline_sales_12mth,na.rm=T), br_net_sales_amt_12_mth = sum(br_net_sales_amt_12_mth,na.rm=T), br_gross_sales_amt = sum(br_gross_sales_amt,na.rm=T))

zip_wise_data <- zip_wise_data[zip_wise_data$addr_zip_code!='',]


library(zipcode)
data("zipcode")
data_dc2<- inner_join(data_dc,zipcode,by=c("addr_zip_code"="zip"))
library("ggplot2")

zip_wise_data <- left_join(zipcode,zip_wise_data,by=c("zip"="addr_zip_code"))
```
Discount Rate by ZipCode

```{r}
zip_wise_data$disc_percent <- (1 - zip_wise_data$br_net_sales_amt_12_mth/zip_wise_data$br_gross_sales_amt )
zip_wise_data$disc_percent[is.na(zip_wise_data$disc_percent)] <- 0
zip_wise_data$disc_percent[zip_wise_data$disc_percent > 1] <- 1
zip_wise_data$disc_percent[zip_wise_data$disc_percent < 0] <- 0

zip_wise_data$Online_percent<-zip_wise_data$br_online_sales12_mth/zip_wise_data$br_net_sales_amt_12_mth
zip_wise_data$Online_percent[is.na(zip_wise_data$Online_percent)] <- 0
zip_wise_data$Online_percent[zip_wise_data$Online_percent > 1] <- 1
zip_wise_data$Online_percent[zip_wise_data$Online_percent < 0] <- 0

zip_wise_data$binx <- cut(zip_wise_data$disc_percent,breaks = c(-Inf,0,0.20,0.35,0.40,Inf),include.lowest = T,
                          labels = c("No_Info","a_Low","b_Medium","c_High","d_VeryHigh"))


zip_wise_data <-zip_wise_data[which(zip_wise_data$latitude < 50 & zip_wise_data$latitude > 25 &
                                    zip_wise_data$longitude > -125 &  zip_wise_data$longitude < -66),]
ggplot(data=zip_wise_data) + geom_point(aes(x=longitude, y=latitude, colour=binx)) +ggtitle(" Discount Rate  ZIPCODE wise") +theme(legend.position ="bottom")

ggplot(zip_wise_data, aes(x = longitude, y = latitude,  fill= disc_percent )) + geom_polygon(colour = "white", size = 0.1) + theme(legend.position ="bottom")

ggplot(zip_wise_data, aes(x = longitude, y = latitude,  fill= Online_percent )) + geom_polygon(colour = "white", size = 0.1) + theme(legend.position ="bottom")

```
Discount Bin 10 is highest Discount
```{r}
require(ggplot2)
require(maptools)
mapcounties <- map_data("county")
mapstates <- map_data("state")

counties <- map('county', fill=TRUE, col="transparent", plot=FALSE)
IDs <- sapply(strsplit(counties$names, ":"), function(x) x[1])
counties_sp <- map2SpatialPolygons(counties, IDs=IDs,
                                   proj4string=CRS("+proj=longlat +datum=WGS84"))

pointsSP <- SpatialPoints(as.data.frame(zip_wise_data[,c('longitude','latitude')]),
proj4string=CRS("+proj=longlat +datum=WGS84"))
indices <- over(pointsSP, counties_sp)

countyNames <- sapply(counties_sp@polygons, function(x) x@ID)

zip_wise_data$county<-countyNames[indices]
# Generate Comma Separated County/Subregion list for all Counties of US
#
mapcounties$county <- with(mapcounties , paste(region, subregion, sep = ","))

# Aggregate our data at County Level (it was earlier aggreated at zipcode level)
#
zip_wise_data.sum<- zip_wise_data %>% group_by(county) %>%  summarize(br_online_sales12_mth = sum(br_online_sales12_mth,na.rm=T), br_offline_sales_12mth = sum(br_offline_sales_12mth,na.rm=T), br_net_sales_amt_12_mth = sum(br_net_sales_amt_12_mth,na.rm=T), br_gross_sales_amt = sum(br_gross_sales_amt,na.rm=T),disc_percent=median(disc_percent,na.rm=T),Online_percent=median(Online_percent))


# Remove any duplicate records
#
zip_wise_data.un<-zip_wise_data.sum[!duplicated(zip_wise_data[c("county")]),]

zip_wise_data.final=merge(zip_wise_data.sum,zip_wise_data.un,by.x='county',by.y = 'county')

zip_wise_data.final$disc_perc_bin <- as.factor(ntile(zip_wise_data.final$disc_percent.x,5))
# Merge our data with all US counties data
#
zip_wise_data.final$Online_percent_bin <- as.factor(ntile(zip_wise_data.final$Online_percent.x,5))
mergedata <- merge(mapcounties, zip_wise_data.final, by.x = "county", by.y = "county")
```
High Discount Rate County Wise
```{r}
map <- ggplot(mergedata, aes(long,lat,group=group)) + geom_polygon(aes(fill=disc_perc_bin)) 
map <- map+theme(panel.background = element_rect(fill = "white"),legend.position = "bottom",
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        axis.text = element_blank())
map <- map + geom_path(data = mapstates, colour = "black", size = .3)
map <- map + geom_path(data = mapcounties, colour = "red", size = .5, alpha = .1)  +
  expand_limits(x = mergedata$long, y = mergedata$lat)
map


```
High Online Sale Percent County Wise

```{r}
ggplot(mergedata, aes(long,lat,group=group)) + geom_polygon(aes(fill=Online_percent_bin)) + theme(panel.background = element_rect(fill = "white"),legend.position = "bottom",
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        axis.text = element_blank()) + geom_path(data = mapstates, colour = "black", size = .3) + geom_path(data = mapcounties, colour = "red", size = .5, alpha = .1)  +
  expand_limits(x = mergedata$long, y = mergedata$lat)

```
Statewide map
```{r}

states_map <-map_data("state")
head(states_map)
library(sqldf)

data_dc3 <-  left_join(zipcode[,c("zip","state")],data_dc2,by=c("zip"="addr_zip_code"))

require(dplyr)
zip_wise_data_state <- data_dc2 %>% group_by(state) %>% summarise(br_online_sales12_mth = sum(br_online_sales12_mth,na.rm=T), br_offline_sales_12mth = sum(br_offline_sales_12mth,na.rm=T), br_net_sales_amt_12_mth = sum(br_net_sales_amt_12_mth,na.rm=T), br_gross_sales_amt = sum(br_gross_sales_amt,na.rm=T))

st_code_file<- data.frame(full_name=c('alabama','alaska','arizona','arkansas','california','colorado',
                                      'connecticut',	'delaware',	'district of columbia',	'florida',	'georgia',
                                      'hawaii',	'idaho',	'illinois',	'indiana',	'iowa',	'kansas',	'kentucky',	
                                      'louisiana',	'maine',	'maryland',	'massachusetts',	'michigan',	'minnesota',
                                      'mississippi',	'missouri',	'montana',	'nebraska',	'nevada',	'new hampshire',
                                      'new jersey',	'new mexico',	'new york',	'north carolina',	'north dakota',	
                                      'ohio',	'oklahoma',	'oregon',	'pennsylvania',	'rhode island',	'south carolina',
                                      'south dakota',	'tennessee',	'texas',	'utah',	'vermont',	'virginia',	'washington',
                                      'west virginia',	'wisconsin',	'wyoming'),
                          state_code=c('AL',	'AK',	'AZ',	'AR',	'CA',	'CO',	'CT',	'DE',	'DC',	'FL',	'GA',	'HI',	'ID',	'IL',
                                       'IN',	'IA',	'KS',	'KY',	'LA',	'ME',	'MD',	'MA',	'MI',	'MN',	'MS',	'MO',	'MT',	'NE',
                                       'NV',	'NH',	'NJ',	'NM',	'NY',	'NC',	'ND',	'OH',	'OK',	'OR',	'PA',	'RI',	'SC',	'SD',
                                       'TN',	'TX',	'UT',	'VT',	'VA',	'WA',	'WV',	'WI',	'WY'))

zip_wise_data_state <- inner_join(st_code_file,zip_wise_data_state,by=c("state_code"="state"))

zip_wise_data_state$disc_percent <- (1 - zip_wise_data_state$br_net_sales_amt_12_mth/zip_wise_data_state$br_gross_sales_amt )
zip_wise_data_state$disc_percent[is.na(zip_wise_data_state$disc_percent)] <- 0
zip_wise_data_state$disc_percent[zip_wise_data_state$disc_percent > 1] <- 1
zip_wise_data_state$disc_percent[zip_wise_data_state$disc_percent < 0] <- 0

zip_wise_data_state$Online_percent <- zip_wise_data_state$br_online_sales12_mth/zip_wise_data_state$br_net_sales_amt_12_mth
zip_wise_data_state$Online_percent[is.na(zip_wise_data_state$Online_percent)] <- 0
zip_wise_data_state$Online_percent[zip_wise_data_state$Online_percent > 1] <- 1
zip_wise_data_state$Online_percent[zip_wise_data_state$Online_percent < 0] <- 0


```
 Discount and % of Online Sales Visualization by State 
```{r}
zip_wise_data_state2 <- zip_wise_data_state[,c("full_name","disc_percent","Online_percent")]
names(zip_wise_data_state2) <- c("state","disc_percent","Online_percent")


ggplot(zip_wise_data_state2, aes(map_id = state)) +
  geom_map(aes(fill = disc_percent), map = states_map, color ="black") +
  expand_limits(x = states_map$long, y = states_map$lat) +
  theme(legend.position = "bottom",
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        axis.text = element_blank()) +
  scale_fill_gradient(low="green", high="red") +
  guides(fill = guide_colorbar(barwidth = 10, barheight = 1))
```
% Online Sales by State
```{r}
ggplot(zip_wise_data_state2, aes(map_id = state)) +
  geom_map(aes(fill = Online_percent), map = states_map, color ="black") +
  expand_limits(x = states_map$long, y = states_map$lat) +
  theme(legend.position = "bottom",
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        axis.text = element_blank()) +
  scale_fill_gradient(low="green", high="red") 
  guides(fill = guide_colorbar(barwidth = 10, barheight = .5))
```

Discount  Percent taken accross United States
```{r}
require(leaflet)

leaflet(zip_wise_data) %>% addTiles() %>%
  addCircles(lng = ~longitude, lat = ~latitude, weight = 1,
             radius = ~(disc_percent-0.3)*100, popup = ~city  )
```

Online Sale Percent over overall sales accross United States

```{r}
require(leaflet)

leaflet(zip_wise_data) %>% addTiles() %>%
  addCircles(lng = ~longitude, lat = ~latitude, weight = 1,
             radius = ~ (Online_percent-0.4)*100, popup = ~city  ,color = "green")
```

